With the completion of an increasing number of genomic sequences, attention is currently focused on how the data contained in sequence databases might be interpreted in terms of the structure, function and control of biological systems. Approaches for global profiling of gene expression at the mRNA level as a function of the cellular state have been developed (DeRisi, et al., Science 278:680–686, 1997; Roth, et al., Nat. Biotechnol, 16:939–945, 1998; Velculescu, et al., Cell, 88:243–251, 1997) and are widely used to identify clusters of genes for which the expression is idiotypic for a specific state. These methods, though exquisitely sensitive, do not indicate changes in protein expression. Quantitative proteome analysis, the global analysis of protein expression, is a complementary method to study steady-state gene expression and perturbation-induced changes. In comparison to gene expression analysis at the mRNA level, proteome analysis provides more accurate information about biological systems and pathways because the measurement directly focuses on the actual biological effector molecules.
Most approaches to quantitative protein analysis are accomplished by combining protein separation, most commonly by high-resolution two-dimensional polyacryamide gel electrophoresis (2D-PAGE), with mass spectrometry (MS)-based or tandem mass spectrometry (MS/MS)-based sequence identification of selected, separated protein species (Link, et al., Electrophoresis, 18:1314–1334, 1997; Shevchenko, et al., PNAS, USA, 93:14440–14445, 1996; Gygi, et al., Mol Cell Biol, 19:1720–1730, 1999; Garreis, et al., Electrophoresis, 18:1347–1360, 1997; Boucheria, et al., Electrophoresis, 17:1683–1699, 1996). This method is sequential, labor intensive and difficult to automate. In addition, it selects against specific classes of proteins, such as membrane proteins, very large and very small proteins and extremely acidic or basic proteins. However, the techniques most significant flaw lies in its bias towards highly abundant proteins, as lower abundant regulatory proteins (e.g., transcription factors, protein kinases, etc.) are rarely detected when total cell lysates are analyzed. (Link, et al., Electrophoresis, 18:1314–1334, 1997; Shevchenko, et al., PNAS, USA, 93:14440–14445, 1996; Gygi, et al., Mol Cell Biol, 19:1720–1730, 1999; Garreis, et al., Electrophoresis, 18:1347–1360, 1997; Boucheria, et al., Electrophoresis, 17:1683–1699, 1996).
The development of methods and instrumentation for automated, data-dependent electrospray ionization (ESI) MS/MS, in conjunction with microcapillary liquid chromatography (mLC) and database searching, has significantly increased the sensitivity and speed for the identification of gel-separated proteins. Moreover, mLC-MS/MS has also been used successfully for the large-scale identification of proteins directly from mixtures without gel electrophoretic separation (Link, et al., Nat Biotechnol, 17:676–682, 1999; Opiteck, et al., Anal Chem, 69:1518–1524, 1997). These analyses, though fast and easily automated, are not quantitative and are also incompatible with the analysis of low-abundance proteins. Thus, there is a great need for a general and quantitative technology for proteome analysis.
One recent development in proteome analysis is the use of a technology called isotope-coded affinity tag (ICAT). However, this technology suffers from an inherent problem whereby steric hindrance caused by the tag often interferes with downstream processing and analysis of the identified and/or isolated protein or protein fragment.
Unfortunately, an easy to use method for the tagging, detection and characterization of molecules that does not have the shortcomings of the prior art methods (e.g., stearic hindrance, radioactivity, and undue complexity of use) is not available. Therefore, what is needed is a novel method for whereby proteins and proteins fragments can be isolated and identified without the interference of stearic hindrance and other prior art problems before any downstream analysis and processing.